Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Chapultepec 1570, Privadas del Pedregal, San Luis Potosí C.P. 78295, Mexico.
Int J Environ Res Public Health. 2023 Jan 8;20(2):1123. doi: 10.3390/ijerph20021123.
Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system's performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.
跌倒事件是一个全球性的健康问题，会对人体造成短期和长期的生理和心理影响，尤其是对老年人群体。本研究旨在监测室内环境中的人体活动，并识别跌倒事件，而无需用户在身体上佩戴设备或传感器。本研究使用通用设备开发了一种基于连续波（CW）射频（RF）探头信号传输的传感平台。CW 探头信号类似于商用现成 WiFi 设备传输的导频副载波。因此，我们的方法可以很容易地集成到联合无线电感知和通信方案中。传感过程是通过分析探头信号在被静止和移动的物体反射或散射时所经历的相位、幅度和频率变化来进行的。这些特征通常从 WiFi 信号的信道状态信息（CSI）中提取。然而，CSI 依赖于复杂的数据采集和信道估计过程。多普勒雷达也被用于监测人体活动。虽然有效，但基于雷达的跌倒检测系统需要专用硬件。在本文中，我们基于一个替代方法，根据 CW 探头信号上由跌倒的人所产生的多普勒特征来描述跌倒事件。为了区分跌倒事件和其他可以在室内环境中进行的活动，设计了一个多类深度学习分类框架。实现了两种神经网络模型。第一种是基于长短期记忆网络（LSTM），第二种是基于卷积神经网络（CNN）。进行了一系列包含 11 名参与者的实验，以收集经验数据并测试系统的性能。对于 LSTM 情况，跌倒的检测准确率为 92.1%，而对于 CNN，准确率为 92.1%。结果表明，基于本文所描述的无线电传感系统进行人体跌倒检测是可行的。
